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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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with health/real grid data or machine learning is beneficial but not mandatory. We Offer One year funded of 3-year PhD position (SIF Doctoral Student Grant). Access to high-performance computing
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machine learning methods to scientific data (e.g. multivariate analysis, chemometrics, neural networks, classification or regression models); familiarity with large language models (LLMs) and other
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analyses. The postdoc will be hosted at TDB, co-supervised by both groups, and will work at the interface of scientific computing, machine learning and particle physics. Project description Searches for dark
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researcher who enjoys developing methods for complex biological data and working closely with experimental scientists. You meet the following criteria: a PhD, or a PhD close to completion, in machine learning
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of the world’s largest research environments in computational science, with large activities in areas such as machine learning, optimization, scientific software development and high-performance computing
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perform both empirical and theoretical work. You will learn how to collect and analyse data within your research area as well as communicate your results at national and international conferences and in
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, or representation learning. Experience analysing large-scale single-cell omics data. Experience with integrative multi-omics data, such as genomics, proteomics, or metabolomics. Experience with relevant machine
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for entry into a PhD program. A background in machine learning, inverse problems, scientific computing, or related data-driven methods is highly desirable. You are curious about combining physical modeling
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concepts into accessible, domain-relevant learning experiences for students. The full job description is available here: https://computing.mit.edu/lecturer/ Job Requirements REQUIRED: PhD in Computer Science